Overview

Dataset statistics

Number of variables24
Number of observations100000
Missing cells36306
Missing cells (%)1.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.8 MiB
Average record size in memory249.9 B

Variable types

DateTime3
Numeric17
Categorical1
Boolean3

Alerts

posa_continent is highly overall correlated with site_nameHigh correlation
site_name is highly overall correlated with posa_continentHigh correlation
srch_destination_id is highly overall correlated with srch_destination_type_idHigh correlation
srch_destination_type_id is highly overall correlated with srch_destination_idHigh correlation
is_booking is highly imbalanced (60.5%)Imbalance
orig_destination_distance has 36068 (36.1%) missing valuesMissing
user_location_region has 1329 (1.3%) zerosZeros
channel has 12462 (12.5%) zerosZeros
srch_children_cnt has 78966 (79.0%) zerosZeros
hotel_continent has 1836 (1.8%) zerosZeros

Reproduction

Analysis started2024-03-21 07:08:49.599326
Analysis finished2024-03-21 07:12:17.408294
Duration3 minutes and 27.81 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Distinct99883
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Minimum2013-01-07 00:00:28
Maximum2014-12-31 23:51:39
2024-03-21T10:12:17.605970image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:12:17.802688image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

site_name
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.85013
Minimum2
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size878.9 KiB
2024-03-21T10:12:18.049569image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median2
Q315
95-th percentile37
Maximum53
Range51
Interquartile range (IQR)13

Descriptive statistics

Standard deviation12.000909
Coefficient of variation (CV)1.2183503
Kurtosis0.054600408
Mean9.85013
Median Absolute Deviation (MAD)0
Skewness1.2457674
Sum985013
Variance144.02181
MonotonicityNot monotonic
2024-03-21T10:12:18.274209image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
2 62940
62.9%
11 6951
 
7.0%
24 6343
 
6.3%
37 5358
 
5.4%
34 4802
 
4.8%
8 2494
 
2.5%
23 1934
 
1.9%
13 1809
 
1.8%
17 1038
 
1.0%
18 700
 
0.7%
Other values (32) 5631
 
5.6%
ValueCountFrequency (%)
2 62940
62.9%
6 42
 
< 0.1%
7 104
 
0.1%
8 2494
 
2.5%
9 97
 
0.1%
10 261
 
0.3%
11 6951
 
7.0%
13 1809
 
1.8%
14 146
 
0.1%
15 232
 
0.2%
ValueCountFrequency (%)
53 28
 
< 0.1%
48 47
 
< 0.1%
47 3
 
< 0.1%
46 122
0.1%
45 7
 
< 0.1%
44 6
 
< 0.1%
43 20
 
< 0.1%
41 3
 
< 0.1%
40 125
0.1%
38 31
 
< 0.1%

posa_continent
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.3 MiB
3
74843 
1
11905 
2
9421 
4
 
3081
0
 
750

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
3 74843
74.8%
1 11905
 
11.9%
2 9421
 
9.4%
4 3081
 
3.1%
0 750
 
0.8%

Length

2024-03-21T10:12:18.464545image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T10:12:18.632150image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
3 74843
74.8%
1 11905
 
11.9%
2 9421
 
9.4%
4 3081
 
3.1%
0 750
 
0.8%

Most occurring characters

ValueCountFrequency (%)
3 74843
74.8%
1 11905
 
11.9%
2 9421
 
9.4%
4 3081
 
3.1%
0 750
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 100000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 74843
74.8%
1 11905
 
11.9%
2 9421
 
9.4%
4 3081
 
3.1%
0 750
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 100000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 74843
74.8%
1 11905
 
11.9%
2 9421
 
9.4%
4 3081
 
3.1%
0 750
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 74843
74.8%
1 11905
 
11.9%
2 9421
 
9.4%
4 3081
 
3.1%
0 750
 
0.8%

user_location_country
Real number (ℝ)

Distinct195
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.28119
Minimum0
Maximum239
Zeros458
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size878.9 KiB
2024-03-21T10:12:18.807273image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q166
median66
Q371
95-th percentile205
Maximum239
Range239
Interquartile range (IQR)5

Descriptive statistics

Standard deviation59.394285
Coefficient of variation (CV)0.68838045
Kurtosis0.27359659
Mean86.28119
Median Absolute Deviation (MAD)0
Skewness1.1249602
Sum8628119
Variance3527.681
MonotonicityNot monotonic
2024-03-21T10:12:18.992987image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66 53775
53.8%
205 11211
 
11.2%
3 5917
 
5.9%
69 5181
 
5.2%
77 2506
 
2.5%
1 2038
 
2.0%
46 1904
 
1.9%
215 1335
 
1.3%
133 1120
 
1.1%
23 825
 
0.8%
Other values (185) 14188
 
14.2%
ValueCountFrequency (%)
0 458
 
0.5%
1 2038
 
2.0%
3 5917
5.9%
4 14
 
< 0.1%
5 191
 
0.2%
6 32
 
< 0.1%
8 9
 
< 0.1%
10 29
 
< 0.1%
11 15
 
< 0.1%
12 308
 
0.3%
ValueCountFrequency (%)
239 18
 
< 0.1%
238 9
 
< 0.1%
237 5
 
< 0.1%
235 170
 
0.2%
234 9
 
< 0.1%
233 2
 
< 0.1%
231 494
0.5%
230 155
 
0.2%
229 113
 
0.1%
228 23
 
< 0.1%

user_location_region
Real number (ℝ)

ZEROS 

Distinct768
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean307.87382
Minimum0
Maximum1027
Zeros1329
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size976.6 KiB
2024-03-21T10:12:19.180560image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38
Q1174
median312
Q3385
95-th percentile790
Maximum1027
Range1027
Interquartile range (IQR)211

Descriptive statistics

Standard deviation208.85329
Coefficient of variation (CV)0.67837302
Kurtosis1.5442031
Mean307.87382
Median Absolute Deviation (MAD)136
Skewness1.151004
Sum30787382
Variance43619.698
MonotonicityNot monotonic
2024-03-21T10:12:19.365222image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
174 10915
 
10.9%
348 4850
 
4.9%
354 4483
 
4.5%
442 3752
 
3.8%
220 3626
 
3.6%
50 2830
 
2.8%
462 2725
 
2.7%
155 2361
 
2.4%
135 2255
 
2.3%
258 2091
 
2.1%
Other values (758) 60112
60.1%
ValueCountFrequency (%)
0 1329
1.3%
1 3
 
< 0.1%
2 4
 
< 0.1%
3 23
 
< 0.1%
4 9
 
< 0.1%
5 14
 
< 0.1%
6 15
 
< 0.1%
7 15
 
< 0.1%
8 3
 
< 0.1%
9 105
 
0.1%
ValueCountFrequency (%)
1027 2
 
< 0.1%
1023 1
 
< 0.1%
1021 2
 
< 0.1%
1017 32
 
< 0.1%
1016 23
 
< 0.1%
1014 1
 
< 0.1%
1013 3
 
< 0.1%
1011 81
0.1%
1010 47
< 0.1%
1008 2
 
< 0.1%

user_location_city
Real number (ℝ)

Distinct10840
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27628.528
Minimum0
Maximum56507
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size976.6 KiB
2024-03-21T10:12:19.555378image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2140
Q112825
median27462
Q342328
95-th percentile53274
Maximum56507
Range56507
Interquartile range (IQR)29503

Descriptive statistics

Standard deviation16752.858
Coefficient of variation (CV)0.60636085
Kurtosis-1.2564649
Mean27628.528
Median Absolute Deviation (MAD)14866
Skewness0.0082890499
Sum2.7628528 × 109
Variance2.8065825 × 108
MonotonicityNot monotonic
2024-03-21T10:12:19.732815image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5703 2083
 
2.1%
48862 1585
 
1.6%
25315 1116
 
1.1%
24103 1097
 
1.1%
36086 950
 
0.9%
2086 746
 
0.7%
14703 745
 
0.7%
35390 685
 
0.7%
41949 669
 
0.7%
4687 645
 
0.6%
Other values (10830) 89679
89.7%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 1
 
< 0.1%
3 23
< 0.1%
14 6
 
< 0.1%
18 1
 
< 0.1%
21 3
 
< 0.1%
25 1
 
< 0.1%
32 9
 
< 0.1%
40 11
< 0.1%
45 1
 
< 0.1%
ValueCountFrequency (%)
56507 6
< 0.1%
56506 1
 
< 0.1%
56498 1
 
< 0.1%
56497 6
< 0.1%
56495 1
 
< 0.1%
56494 1
 
< 0.1%
56492 8
< 0.1%
56491 1
 
< 0.1%
56488 1
 
< 0.1%
56480 1
 
< 0.1%

orig_destination_distance
Real number (ℝ)

MISSING 

Distinct62218
Distinct (%)97.3%
Missing36068
Missing (%)36.1%
Infinite0
Infinite (%)0.0%
Mean1965.4872
Minimum0.0055999998
Maximum11766.433
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-03-21T10:12:19.921006image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.0055999998
5-th percentile43.678089
Q1313.18181
median1131.7756
Q32543.5016
95-th percentile6869.7212
Maximum11766.433
Range11766.427
Interquartile range (IQR)2230.3198

Descriptive statistics

Standard deviation2233.1101
Coefficient of variation (CV)1.1361611
Kurtosis2.155371
Mean1965.4872
Median Absolute Deviation (MAD)940.74377
Skewness1.6088099
Sum1.2565753 × 108
Variance4986781
MonotonicityNot monotonic
2024-03-21T10:12:20.119571image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1036.196411 9
 
< 0.1%
1953.148071 7
 
< 0.1%
226.7472992 7
 
< 0.1%
227.2722931 6
 
< 0.1%
0.0055999998 6
 
< 0.1%
43.26950073 6
 
< 0.1%
65.15660095 5
 
< 0.1%
1036.575928 5
 
< 0.1%
227.392807 5
 
< 0.1%
2235.612305 5
 
< 0.1%
Other values (62208) 63871
63.9%
(Missing) 36068
36.1%
ValueCountFrequency (%)
0.0055999998 6
< 0.1%
0.01710000075 1
 
< 0.1%
0.02280000038 1
 
< 0.1%
0.02979999967 1
 
< 0.1%
0.04030000046 1
 
< 0.1%
0.04149999842 1
 
< 0.1%
0.04540000111 1
 
< 0.1%
0.04659999907 1
 
< 0.1%
0.05600000173 1
 
< 0.1%
0.06750000268 1
 
< 0.1%
ValueCountFrequency (%)
11766.43262 1
< 0.1%
11669.33691 1
< 0.1%
11662.52832 1
< 0.1%
11646.31934 1
< 0.1%
11635.64844 1
< 0.1%
11634.80664 1
< 0.1%
11632.24707 1
< 0.1%
11631.99805 1
< 0.1%
11631.92383 1
< 0.1%
11631.54395 1
< 0.1%

user_id
Real number (ℝ)

Distinct89008
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean605671.12
Minimum5
Maximum1198761
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-03-21T10:12:20.353765image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile58909.25
Q1299868
median606661.5
Q3911002.5
95-th percentile1149283.3
Maximum1198761
Range1198756
Interquartile range (IQR)611134.5

Descriptive statistics

Standard deviation350453.24
Coefficient of variation (CV)0.57861971
Kurtosis-1.2189668
Mean605671.12
Median Absolute Deviation (MAD)305554.5
Skewness-0.014230223
Sum6.0567112 × 1010
Variance1.2281748 × 1011
MonotonicityNot monotonic
2024-03-21T10:12:20.553498image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
414572 6
 
< 0.1%
515280 6
 
< 0.1%
269580 6
 
< 0.1%
9614 5
 
< 0.1%
509838 5
 
< 0.1%
612161 5
 
< 0.1%
843234 5
 
< 0.1%
228821 5
 
< 0.1%
1148446 5
 
< 0.1%
1135501 5
 
< 0.1%
Other values (88998) 99947
99.9%
ValueCountFrequency (%)
5 1
< 0.1%
34 1
< 0.1%
65 1
< 0.1%
81 1
< 0.1%
99 1
< 0.1%
107 1
< 0.1%
113 1
< 0.1%
115 1
< 0.1%
120 1
< 0.1%
130 1
< 0.1%
ValueCountFrequency (%)
1198761 1
< 0.1%
1198751 1
< 0.1%
1198706 1
< 0.1%
1198680 1
< 0.1%
1198665 1
< 0.1%
1198663 1
< 0.1%
1198642 1
< 0.1%
1198640 1
< 0.1%
1198635 1
< 0.1%
1198626 1
< 0.1%

is_mobile
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size878.9 KiB
False
86631 
True
13369 
ValueCountFrequency (%)
False 86631
86.6%
True 13369
 
13.4%
2024-03-21T10:12:20.721011image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

is_package
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size878.9 KiB
False
75146 
True
24854 
ValueCountFrequency (%)
False 75146
75.1%
True 24854
 
24.9%
2024-03-21T10:12:20.838045image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

channel
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.87375
Minimum0
Maximum10
Zeros12462
Zeros (%)12.5%
Negative0
Negative (%)0.0%
Memory size878.9 KiB
2024-03-21T10:12:20.965964image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median9
Q39
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.7182831
Coefficient of variation (CV)0.63303394
Kurtosis-1.5376652
Mean5.87375
Median Absolute Deviation (MAD)0
Skewness-0.50086065
Sum587375
Variance13.825629
MonotonicityNot monotonic
2024-03-21T10:12:21.105995image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
9 55498
55.5%
0 12462
 
12.5%
1 10100
 
10.1%
2 7813
 
7.8%
5 6048
 
6.0%
3 4609
 
4.6%
4 2135
 
2.1%
7 856
 
0.9%
8 295
 
0.3%
6 158
 
0.2%
ValueCountFrequency (%)
0 12462
 
12.5%
1 10100
 
10.1%
2 7813
 
7.8%
3 4609
 
4.6%
4 2135
 
2.1%
5 6048
 
6.0%
6 158
 
0.2%
7 856
 
0.9%
8 295
 
0.3%
9 55498
55.5%
ValueCountFrequency (%)
10 26
 
< 0.1%
9 55498
55.5%
8 295
 
0.3%
7 856
 
0.9%
6 158
 
0.2%
5 6048
 
6.0%
4 2135
 
2.1%
3 4609
 
4.6%
2 7813
 
7.8%
1 10100
 
10.1%
Distinct1066
Distinct (%)1.1%
Missing119
Missing (%)0.1%
Memory size1.5 MiB
Minimum2013-01-07 00:00:00
Maximum2016-01-20 00:00:00
2024-03-21T10:12:21.301325image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:12:21.500290image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1069
Distinct (%)1.1%
Missing119
Missing (%)0.1%
Memory size1.5 MiB
Minimum2013-01-08 00:00:00
Maximum2016-01-26 00:00:00
2024-03-21T10:12:21.680251image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:12:21.866246image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

srch_adults_cnt
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.02116
Minimum0
Maximum9
Zeros156
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size878.9 KiB
2024-03-21T10:12:22.028189image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q32
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.90447799
Coefficient of variation (CV)0.4475044
Kurtosis9.4779836
Mean2.02116
Median Absolute Deviation (MAD)0
Skewness2.3179726
Sum202116
Variance0.81808044
MonotonicityNot monotonic
2024-03-21T10:12:22.183763image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 65945
65.9%
1 21334
 
21.3%
3 5297
 
5.3%
4 5268
 
5.3%
6 859
 
0.9%
5 752
 
0.8%
8 216
 
0.2%
0 156
 
0.2%
7 135
 
0.1%
9 38
 
< 0.1%
ValueCountFrequency (%)
0 156
 
0.2%
1 21334
 
21.3%
2 65945
65.9%
3 5297
 
5.3%
4 5268
 
5.3%
5 752
 
0.8%
6 859
 
0.9%
7 135
 
0.1%
8 216
 
0.2%
9 38
 
< 0.1%
ValueCountFrequency (%)
9 38
 
< 0.1%
8 216
 
0.2%
7 135
 
0.1%
6 859
 
0.9%
5 752
 
0.8%
4 5268
 
5.3%
3 5297
 
5.3%
2 65945
65.9%
1 21334
 
21.3%
0 156
 
0.2%

srch_children_cnt
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.33202
Minimum0
Maximum9
Zeros78966
Zeros (%)79.0%
Negative0
Negative (%)0.0%
Memory size878.9 KiB
2024-03-21T10:12:22.345234image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7297863
Coefficient of variation (CV)2.1980191
Kurtosis7.8084913
Mean0.33202
Median Absolute Deviation (MAD)0
Skewness2.5342589
Sum33202
Variance0.53258805
MonotonicityNot monotonic
2024-03-21T10:12:22.510679image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 78966
79.0%
1 11280
 
11.3%
2 7995
 
8.0%
3 1290
 
1.3%
4 366
 
0.4%
5 46
 
< 0.1%
6 37
 
< 0.1%
7 16
 
< 0.1%
9 2
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
0 78966
79.0%
1 11280
 
11.3%
2 7995
 
8.0%
3 1290
 
1.3%
4 366
 
0.4%
5 46
 
< 0.1%
6 37
 
< 0.1%
7 16
 
< 0.1%
8 2
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
9 2
 
< 0.1%
8 2
 
< 0.1%
7 16
 
< 0.1%
6 37
 
< 0.1%
5 46
 
< 0.1%
4 366
 
0.4%
3 1290
 
1.3%
2 7995
 
8.0%
1 11280
 
11.3%
0 78966
79.0%

srch_rm_cnt
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.11061
Minimum0
Maximum8
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size878.9 KiB
2024-03-21T10:12:22.653672image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4533183
Coefficient of variation (CV)0.40817056
Kurtosis70.710453
Mean1.11061
Median Absolute Deviation (MAD)0
Skewness6.9115043
Sum111061
Variance0.20549748
MonotonicityNot monotonic
2024-03-21T10:12:22.823435image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 91814
91.8%
2 6521
 
6.5%
3 1092
 
1.1%
4 274
 
0.3%
5 137
 
0.1%
8 78
 
0.1%
6 50
 
0.1%
7 32
 
< 0.1%
0 2
 
< 0.1%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 91814
91.8%
2 6521
 
6.5%
3 1092
 
1.1%
4 274
 
0.3%
5 137
 
0.1%
6 50
 
0.1%
7 32
 
< 0.1%
8 78
 
0.1%
ValueCountFrequency (%)
8 78
 
0.1%
7 32
 
< 0.1%
6 50
 
0.1%
5 137
 
0.1%
4 274
 
0.3%
3 1092
 
1.1%
2 6521
 
6.5%
1 91814
91.8%
0 2
 
< 0.1%

srch_destination_id
Real number (ℝ)

HIGH CORRELATION 

Distinct8878
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14444.046
Minimum4
Maximum65035
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size976.6 KiB
2024-03-21T10:12:23.024049image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile1691
Q18267
median9147
Q318788
95-th percentile42630
Maximum65035
Range65031
Interquartile range (IQR)10521

Descriptive statistics

Standard deviation11068.115
Coefficient of variation (CV)0.76627523
Kurtosis3.8498045
Mean14444.046
Median Absolute Deviation (MAD)2861
Skewness1.9022011
Sum1.4444046 × 109
Variance1.2250317 × 108
MonotonicityNot monotonic
2024-03-21T10:12:23.238445image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8250 3524
 
3.5%
8267 2691
 
2.7%
8791 1665
 
1.7%
8268 1393
 
1.4%
8253 1341
 
1.3%
8745 1314
 
1.3%
8279 1162
 
1.2%
11439 991
 
1.0%
8260 902
 
0.9%
12206 895
 
0.9%
Other values (8868) 84122
84.1%
ValueCountFrequency (%)
4 3
 
< 0.1%
8 9
 
< 0.1%
9 1
 
< 0.1%
11 3
 
< 0.1%
14 3
 
< 0.1%
16 2
 
< 0.1%
19 3
 
< 0.1%
21 28
< 0.1%
23 1
 
< 0.1%
24 14
< 0.1%
ValueCountFrequency (%)
65035 1
< 0.1%
65019 1
< 0.1%
64988 1
< 0.1%
64986 1
< 0.1%
64982 1
< 0.1%
64940 1
< 0.1%
64926 1
< 0.1%
64925 1
< 0.1%
64884 1
< 0.1%
64871 2
< 0.1%

srch_destination_type_id
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.57972
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size878.9 KiB
2024-03-21T10:12:23.421047image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35
95-th percentile6
Maximum9
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.1502444
Coefficient of variation (CV)0.83351852
Kurtosis-1.1650403
Mean2.57972
Median Absolute Deviation (MAD)0
Skewness0.78875045
Sum257972
Variance4.623551
MonotonicityNot monotonic
2024-03-21T10:12:23.649591image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 61883
61.9%
6 22413
 
22.4%
3 7308
 
7.3%
5 4754
 
4.8%
4 3305
 
3.3%
8 328
 
0.3%
9 5
 
< 0.1%
7 4
 
< 0.1%
ValueCountFrequency (%)
1 61883
61.9%
3 7308
 
7.3%
4 3305
 
3.3%
5 4754
 
4.8%
6 22413
 
22.4%
7 4
 
< 0.1%
8 328
 
0.3%
9 5
 
< 0.1%
ValueCountFrequency (%)
9 5
 
< 0.1%
8 328
 
0.3%
7 4
 
< 0.1%
6 22413
 
22.4%
5 4754
 
4.8%
4 3305
 
3.3%
3 7308
 
7.3%
1 61883
61.9%

is_booking
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size878.9 KiB
False
92211 
True
 
7789
ValueCountFrequency (%)
False 92211
92.2%
True 7789
 
7.8%
2024-03-21T10:12:23.830458image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

cnt
Real number (ℝ)

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.48251
Minimum1
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size976.6 KiB
2024-03-21T10:12:23.994001image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum44
Range43
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2028751
Coefficient of variation (CV)0.81137741
Kurtosis76.06216
Mean1.48251
Median Absolute Deviation (MAD)0
Skewness5.9854761
Sum148251
Variance1.4469086
MonotonicityNot monotonic
2024-03-21T10:12:24.166995image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1 74398
74.4%
2 15260
 
15.3%
3 5452
 
5.5%
4 2193
 
2.2%
5 1124
 
1.1%
6 614
 
0.6%
7 343
 
0.3%
8 211
 
0.2%
9 125
 
0.1%
10 81
 
0.1%
Other values (22) 199
 
0.2%
ValueCountFrequency (%)
1 74398
74.4%
2 15260
 
15.3%
3 5452
 
5.5%
4 2193
 
2.2%
5 1124
 
1.1%
6 614
 
0.6%
7 343
 
0.3%
8 211
 
0.2%
9 125
 
0.1%
10 81
 
0.1%
ValueCountFrequency (%)
44 1
 
< 0.1%
33 1
 
< 0.1%
32 1
 
< 0.1%
30 1
 
< 0.1%
29 1
 
< 0.1%
28 2
< 0.1%
26 2
< 0.1%
25 1
 
< 0.1%
24 3
< 0.1%
23 3
< 0.1%

hotel_continent
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.15373
Minimum0
Maximum6
Zeros1836
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size878.9 KiB
2024-03-21T10:12:24.329215image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median2
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6196059
Coefficient of variation (CV)0.5135525
Kurtosis-0.66575015
Mean3.15373
Median Absolute Deviation (MAD)0
Skewness0.79172655
Sum315373
Variance2.6231233
MonotonicityNot monotonic
2024-03-21T10:12:24.484114image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 52594
52.6%
6 20023
 
20.0%
3 13112
 
13.1%
4 11464
 
11.5%
0 1836
 
1.8%
5 971
 
1.0%
ValueCountFrequency (%)
0 1836
 
1.8%
2 52594
52.6%
3 13112
 
13.1%
4 11464
 
11.5%
5 971
 
1.0%
6 20023
 
20.0%
ValueCountFrequency (%)
6 20023
 
20.0%
5 971
 
1.0%
4 11464
 
11.5%
3 13112
 
13.1%
2 52594
52.6%
0 1836
 
1.8%

hotel_country
Real number (ℝ)

Distinct176
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.39069
Minimum0
Maximum212
Zeros151
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size878.9 KiB
2024-03-21T10:12:24.693217image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q150
median50
Q3106
95-th percentile198
Maximum212
Range212
Interquartile range (IQR)56

Descriptive statistics

Standard deviation56.144171
Coefficient of variation (CV)0.68981072
Kurtosis-0.18801869
Mean81.39069
Median Absolute Deviation (MAD)3
Skewness1.0231865
Sum8139069
Variance3152.1679
MonotonicityNot monotonic
2024-03-21T10:12:24.898316image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 47915
47.9%
8 5039
 
5.0%
198 4679
 
4.7%
105 3579
 
3.6%
70 3204
 
3.2%
204 2810
 
2.8%
182 2444
 
2.4%
77 2433
 
2.4%
106 1765
 
1.8%
144 1584
 
1.6%
Other values (166) 24548
24.5%
ValueCountFrequency (%)
0 151
 
0.2%
1 47
 
< 0.1%
2 13
 
< 0.1%
3 13
 
< 0.1%
4 33
 
< 0.1%
5 851
 
0.9%
7 268
 
0.3%
8 5039
5.0%
9 25
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
212 16
 
< 0.1%
211 7
 
< 0.1%
210 2
 
< 0.1%
209 1
 
< 0.1%
208 712
 
0.7%
206 42
 
< 0.1%
205 1
 
< 0.1%
204 2810
2.8%
203 186
 
0.2%
202 71
 
0.1%

hotel_market
Real number (ℝ)

Distinct1867
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean599.93135
Minimum0
Maximum2117
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size976.6 KiB
2024-03-21T10:12:25.077693image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19
Q1159
median592
Q3701
95-th percentile1614
Maximum2117
Range2117
Interquartile range (IQR)542

Descriptive statistics

Standard deviation511.41198
Coefficient of variation (CV)0.85245083
Kurtosis0.16599225
Mean599.93135
Median Absolute Deviation (MAD)370
Skewness0.95903326
Sum59993135
Variance261542.21
MonotonicityNot monotonic
2024-03-21T10:12:25.247400image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
628 4691
 
4.7%
675 4288
 
4.3%
682 2288
 
2.3%
19 2128
 
2.1%
365 2010
 
2.0%
701 2005
 
2.0%
110 1993
 
2.0%
27 1723
 
1.7%
1230 1647
 
1.6%
212 1376
 
1.4%
Other values (1857) 75851
75.9%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 4
 
< 0.1%
2 859
0.9%
3 16
 
< 0.1%
4 436
0.4%
5 211
 
0.2%
6 117
 
0.1%
7 89
 
0.1%
8 244
 
0.2%
9 6
 
< 0.1%
ValueCountFrequency (%)
2117 10
 
< 0.1%
2116 1
 
< 0.1%
2114 1
 
< 0.1%
2113 3
 
< 0.1%
2112 2
 
< 0.1%
2111 30
< 0.1%
2109 2
 
< 0.1%
2108 5
 
< 0.1%
2107 9
 
< 0.1%
2106 2
 
< 0.1%

hotel_cluster
Real number (ℝ)

Distinct100
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.82681
Minimum0
Maximum99
Zeros969
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size878.9 KiB
2024-03-21T10:12:25.424083image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q125
median49
Q373
95-th percentile96
Maximum99
Range99
Interquartile range (IQR)48

Descriptive statistics

Standard deviation28.923766
Coefficient of variation (CV)0.580486
Kurtosis-1.1503802
Mean49.82681
Median Absolute Deviation (MAD)24
Skewness0.0062483961
Sum4982681
Variance836.58422
MonotonicityNot monotonic
2024-03-21T10:12:25.607536image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 2701
 
2.7%
41 2101
 
2.1%
48 1981
 
2.0%
64 1817
 
1.8%
65 1749
 
1.7%
5 1648
 
1.6%
98 1521
 
1.5%
59 1484
 
1.5%
21 1455
 
1.5%
83 1451
 
1.5%
Other values (90) 82092
82.1%
ValueCountFrequency (%)
0 969
1.0%
1 1193
1.2%
2 1146
1.1%
3 573
 
0.6%
4 897
0.9%
5 1648
1.6%
6 1013
1.0%
7 695
0.7%
8 889
0.9%
9 1300
1.3%
ValueCountFrequency (%)
99 1237
1.2%
98 1521
1.5%
97 1281
1.3%
96 1020
 
1.0%
95 1374
1.4%
94 818
 
0.8%
93 568
 
0.6%
92 646
 
0.6%
91 2701
2.7%
90 1107
1.1%

Interactions

2024-03-21T10:12:13.162044image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:06.436162image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:08.895581image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:11.760473image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:14.548048image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:17.115288image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:19.627482image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:22.482393image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:24.886412image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:27.585830image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:10:45.416595image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:11:47.130331image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:11:50.147458image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:11:53.176037image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:12:04.876381image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:12:07.754427image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:12:10.420999image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:12:13.288125image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:06.622867image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:09.034559image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:11.896033image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:14.686298image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:17.243161image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:19.762323image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:22.626592image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:25.022032image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:27.719098image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:10:45.597866image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:11:47.286475image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:11:50.337383image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:11:53.341989image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:12:05.067064image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:12:07.894275image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:12:10.562200image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:12:13.422013image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:06.767703image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:09.177168image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:12.039707image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:14.836469image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:17.387130image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:19.896184image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:22.775403image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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2024-03-21T10:09:22.010047image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:24.478829image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:27.197552image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:10:44.949138image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:11:46.596139image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:11:49.637433image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:11:52.753085image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:12:04.451031image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:12:07.314270image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:12:09.937279image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:12:12.689173image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:12:15.214106image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:08.585980image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:11.440422image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:14.275146image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:16.847339image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:19.359696image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:22.147276image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:24.603471image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:27.320368image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:10:45.106386image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:11:46.757976image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:11:49.812316image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:11:52.902347image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:12:04.597514image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:12:07.457584image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:12:10.078429image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:12:12.850143image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:12:15.360013image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:08.706345image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:11.613073image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:14.404450image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:16.978215image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:19.496473image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:22.294306image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:24.752588image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:09:27.446450image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:10:45.251055image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:11:46.955285image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:11:49.974106image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:11:53.038003image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:12:04.729339image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:12:07.603456image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:12:10.255259image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T10:12:12.991329image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2024-03-21T10:12:25.780038image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
channelcnthotel_clusterhotel_continenthotel_countryhotel_marketis_bookingis_mobileis_packageorig_destination_distanceposa_continentsite_namesrch_adults_cntsrch_children_cntsrch_destination_idsrch_destination_type_idsrch_rm_cntuser_iduser_location_cityuser_location_countryuser_location_region
channel1.000-0.0140.008-0.031-0.0180.0170.0400.1010.0940.0010.156-0.062-0.0370.013-0.0040.0180.0040.0020.0270.0800.014
cnt-0.0141.0000.0020.021-0.006-0.0120.0360.0070.0950.0350.0050.0170.0330.036-0.013-0.0110.0060.0010.000-0.000-0.005
hotel_cluster0.0080.0021.0000.001-0.0350.0360.0410.0100.1340.0240.054-0.0270.0220.016-0.016-0.037-0.0060.0040.000-0.0060.013
hotel_continent-0.0310.0210.0011.0000.353-0.3030.0580.0480.2990.4920.3720.228-0.017-0.0510.024-0.0690.0280.0060.002-0.050-0.033
hotel_country-0.018-0.006-0.0350.3531.000-0.1040.0460.0380.2340.1890.2350.306-0.036-0.0390.068-0.0230.0100.012-0.0110.044-0.081
hotel_market0.017-0.0120.036-0.303-0.1041.0000.0490.0280.192-0.1930.174-0.1240.0180.0120.1030.047-0.008-0.0070.0090.0390.076
is_booking0.0400.0360.0410.0580.0460.0491.0000.0320.075-0.0570.023-0.012-0.070-0.0190.0230.0430.0080.003-0.0010.0060.006
is_mobile0.1010.0070.0100.0480.0380.0280.0321.0000.053-0.0590.039-0.0170.0330.0170.001-0.021-0.020-0.0050.0020.0170.018
is_package0.0940.0950.1340.2990.2340.1920.0750.0531.0000.1900.1220.0460.004-0.039-0.149-0.234-0.028-0.0080.0140.0040.041
orig_destination_distance0.0010.0350.0240.4920.189-0.193-0.057-0.0590.1901.0000.2030.084-0.010-0.047-0.081-0.078-0.0100.0100.0230.1000.040
posa_continent0.1560.0050.0540.3720.2350.1740.0230.0390.1220.2031.000-0.6380.0330.0340.0240.049-0.031-0.0110.0660.2150.160
site_name-0.0620.017-0.0270.2280.306-0.124-0.012-0.0170.0460.084-0.6381.000-0.013-0.0160.007-0.0110.0090.026-0.0200.235-0.015
srch_adults_cnt-0.0370.0330.022-0.017-0.0360.018-0.0700.0330.004-0.0100.033-0.0131.0000.1190.007-0.0250.426-0.0050.0050.0470.022
srch_children_cnt0.0130.0360.016-0.051-0.0390.012-0.0190.017-0.039-0.0470.034-0.0160.1191.0000.010-0.0050.090-0.0040.0080.0260.013
srch_destination_id-0.004-0.013-0.0160.0240.0680.1030.0230.001-0.149-0.0810.0240.0070.0070.0101.0000.5180.0080.0030.0050.0120.021
srch_destination_type_id0.018-0.011-0.037-0.069-0.0230.0470.043-0.021-0.234-0.0780.049-0.011-0.025-0.0050.5181.0000.0070.011-0.0010.0250.007
srch_rm_cnt0.0040.006-0.0060.0280.010-0.0080.008-0.020-0.028-0.010-0.0310.0090.4260.0900.0080.0071.0000.004-0.002-0.003-0.010
user_id0.0020.0010.0040.0060.012-0.0070.003-0.005-0.0080.010-0.0110.026-0.005-0.0040.0030.0110.0041.000-0.005-0.018-0.010
user_location_city0.0270.0000.0000.002-0.0110.009-0.0010.0020.0140.0230.066-0.0200.0050.0080.005-0.001-0.002-0.0051.0000.1210.136
user_location_country0.080-0.000-0.006-0.0500.0440.0390.0060.0170.0040.1000.2150.2350.0470.0260.0120.025-0.003-0.0180.1211.0000.198
user_location_region0.014-0.0050.013-0.033-0.0810.0760.0060.0180.0410.0400.160-0.0150.0220.0130.0210.007-0.010-0.0100.1360.1981.000

Missing values

2024-03-21T10:12:15.699048image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-21T10:12:16.291436image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-21T10:12:16.970095image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

date_timesite_nameposa_continentuser_location_countryuser_location_regionuser_location_cityorig_destination_distanceuser_idis_mobileis_packagechannelsrch_cisrch_cosrch_adults_cntsrch_children_cntsrch_rm_cntsrch_destination_idsrch_destination_type_idis_bookingcnthotel_continenthotel_countryhotel_markethotel_cluster
118379702014-08-01 06:09:4023661962428206.7729951063336FalseFalse12014-11-142014-11-1520176353False225067556
191431002014-06-16 08:28:202366348212914367.375000229874FalseFalse02014-08-262014-08-28301178231False1610577036
7398742013-09-15 14:51:262366318289941971.32763771596FalseTrue22013-12-242013-12-3122182541True125036537
38276362014-12-08 19:52:08236617418877334.765198290929FalseFalse92014-12-122014-12-13201235416False12506609
363184212014-12-15 22:29:2724236412805NaN1075410FalseFalse42014-12-272014-12-29201203321True1312626414
35603182014-05-12 14:27:122366174376752587.924072838867FalseFalse12014-07-132014-07-19101121846False125069091
27878212014-11-16 23:14:162366142174402336.1840821187727FalseFalse22014-11-192014-11-21201132374False32503654
197010682014-03-06 07:43:322366348533772428.343262555746FalseTrue92014-12-182014-12-2140188241False14811852
9373082014-10-29 20:45:5726021564647902NaN689139FalseFalse22014-12-102014-12-11201139256False14812644
42577402013-06-21 18:38:172423505703NaN212812FalseFalse12013-09-042013-09-0910187991False16204146335
date_timesite_nameposa_continentuser_location_countryuser_location_regionuser_location_cityorig_destination_distanceuser_idis_mobileis_packagechannelsrch_cisrch_cosrch_adults_cntsrch_children_cntsrch_rm_cntsrch_destination_idsrch_destination_type_idis_bookingcnthotel_continenthotel_countryhotel_markethotel_cluster
146630912013-02-17 23:44:53231275246545NaN500738FalseFalse22013-05-312013-06-1021117251False43182149357
265774192014-07-17 17:06:44236622019222205.5789031010517FalseFalse92014-07-272014-08-0113182601False12507015
29077162014-10-31 14:45:44260215646517331450.634399307336FalseFalse52014-12-272015-01-01241113731False14128145596
231726552014-07-25 07:32:5223663213263139.23199578779FalseFalse92014-08-132014-08-141014111False125047022
354857902014-09-17 19:23:522366348479971585.335205301885TrueTrue92014-09-232014-09-2640254056False44812652
137571382013-05-30 21:50:223716986654284NaN240138FalseFalse92013-12-012013-12-1121182681False125068248
193187622014-07-22 11:43:542366448380237920.593262710825FalseFalse92014-11-202014-11-211014073False15122146257
9695652014-06-11 07:57:17236644814052365.342804802904FalseFalse92014-06-122014-06-13201174561False125043748
273245552014-08-18 16:21:2424236412576NaN774191FalseFalse22014-08-202014-08-23101187741False139912393
5559232013-08-04 09:56:172366340321935988.224609685138FalseFalse92013-08-292013-09-02301123335True1620210429